<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0123-7799</journal-id>
<journal-title><![CDATA[TecnoLógicas]]></journal-title>
<abbrev-journal-title><![CDATA[TecnoL.]]></abbrev-journal-title>
<issn>0123-7799</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico Metropolitano - ITM]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-77992024000200207</article-id>
<article-id pub-id-type="doi">10.22430/22565337.3052</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Postcontrast Medical Image Synthesis in Breast DCE-MRI Using Deep Learning]]></article-title>
<article-title xml:lang="es"><![CDATA[Síntesis de imagen médica postcontraste en estudios de DCE-MRI de mama usando aprendizaje profundo]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cañaveral]]></surname>
<given-names><![CDATA[Sara]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mera-Banguero]]></surname>
<given-names><![CDATA[Carlos]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fonnegra]]></surname>
<given-names><![CDATA[Rubén D.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Tecnológico Metropolitano  ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Tecnológico Metropolitano; Universidad de Antioquia  ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Institución Universitaria Pascual Bravo  ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2024</year>
</pub-date>
<volume>27</volume>
<numero>60</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-77992024000200207&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-77992024000200207&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-77992024000200207&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Breast cancer is one of the leading causes of death in women in the world, so its early detection has become a priority to save lives. For the diagnosis of this type of cancer, there are techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which uses a contrast agent to enhance abnormalities in breast tissue, which improves the detection and characterization of possible tumors. As a limitation, DCE-MRI studies are usually expensive, there is little equipment available to perform them, and in some cases the contrast medium can generate adverse effects due to an allergic reaction. Considering all of the above, the aim of this work was to use deep learning models for the generation of postcontrast synthetic images in DCE-MRI studies. The proposed methodology consisted of the development of a cost function, called CeR-Loss, that takes advantage of the contrast agent uptake behavior. As a result, two new deep learning architectures were trained, which we have named G-RiedGAN and D-RiedGAN, for the generation of postcontrast images in DCE-MRI studies, from precontrast images. Finally, it is concluded that the peak signal-to-noise ratio, structured similarity indexing method, and mean absolute error metrics show that the proposed architectures improve the postcontrast image synthesis process, preserving greater similarity between the synthetic images and the real images, compared to the state-of-the-art base models.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El cáncer de mama es una de las principales causas de muerte en mujeres en el mundo, por lo que su detección de forma temprana se ha convertido en una prioridad para salvar vidas. Para el diagnóstico de este tipo de cáncer existen técnicas como la imagen de resonancia magnética dinámica con realce de contraste (DCE-MRI, por sus siglas en inglés), la cual usa un agente de contraste para realzar las anomalías en el tejido de la mama, lo que mejora la detección y caracterización de posibles tumores. Como limitación, los estudios de DCE-MRI suelen tener un costo alto, hay poca disponibilidad de equipos para realizarlos, y en algunos casos los medios de contraste pueden generar efectos adversos por reacciones alérgicas. Considerando lo anterior, este trabajo tuvo como objetivo el uso de modelos de aprendizaje profundo para la generación de imágenes sintéticas postcontraste en estudios de DCE-MRI. La metodología consistió en el desarrollo de una función de costo denominada pérdida en las regiones con realce de contraste que aprovecha el comportamiento de la captación del agente de contraste. Como resultado se entrenaron dos nuevas arquitecturas de aprendizaje profundo, las cuales hemos denominado G-RiedGAN y D-RiedGAN, para la generación de imágenes postcontraste en estudios de DCE-MRI, a partir de imágenes precontraste. Finalmente, se concluye que las métricas proporción máxima señal ruido, índice de similitud estructural y error absoluto medio muestran que las arquitecturas propuestas mejoran el proceso de síntesis de las imágenes postcontraste preservando mayor similitud entre las imágenes sintéticas y las imágenes reales, esto en comparación con los modelos base en el estado del arte.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Breast cancer]]></kwd>
<kwd lng="en"><![CDATA[diagnostic imaging]]></kwd>
<kwd lng="en"><![CDATA[magnetic resonance imaging]]></kwd>
<kwd lng="en"><![CDATA[postcontrast image generation]]></kwd>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="es"><![CDATA[Cáncer de mama]]></kwd>
<kwd lng="es"><![CDATA[imagen médica]]></kwd>
<kwd lng="es"><![CDATA[resonancia magnética]]></kwd>
<kwd lng="es"><![CDATA[generación de imagen postcontraste]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje profundo]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>[1]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jimenez Herrera]]></surname>
<given-names><![CDATA[M. P.]]></given-names>
</name>
</person-group>
<source><![CDATA[Informe de Evento Cáncer de Mama y Cuello Uterino en Colombia 2018]]></source>
<year>2018</year>
<publisher-loc><![CDATA[Colombia ]]></publisher-loc>
<publisher-name><![CDATA[Instituto Nacional de Salud]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B2">
<label>[2]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Martín]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Herrero]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Echavarría]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[El cáncer de mama]]></article-title>
<source><![CDATA[Arbor]]></source>
<year>2015</year>
<volume>191</volume>
<numero>773</numero>
<issue>773</issue>
<page-range>a234</page-range></nlm-citation>
</ref>
<ref id="B3">
<label>[3]</label><nlm-citation citation-type="book">
<collab>IARC</collab>
<source><![CDATA[Data visualization tools for exploring the global cancer burden in 2022]]></source>
<year>2024</year>
<publisher-name><![CDATA[iarc.who]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B4">
<label>[4]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks]]></article-title>
<source><![CDATA[IEEE Access]]></source>
<year>2020</year>
<volume>8</volume>
<page-range>90931-56</page-range></nlm-citation>
</ref>
<ref id="B5">
<label>[5]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Guleria]]></surname>
<given-names><![CDATA[H. V.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Enhancing the breast histopathology image analysis for cancer detection using Variational Autoencoder]]></article-title>
<source><![CDATA[Int. J. Environ. Res. Public Health.]]></source>
<year>2023</year>
<volume>20</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>4244</page-range></nlm-citation>
</ref>
<ref id="B6">
<label>[6]</label><nlm-citation citation-type="book">
<collab>Instituto Nacional del Cáncer</collab>
<source><![CDATA[Tratamiento del cáncer de seno]]></source>
<year>2024</year>
<publisher-name><![CDATA[cancer.gov.]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B7">
<label>[7]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Macias]]></surname>
<given-names><![CDATA[S. G.]]></given-names>
</name>
</person-group>
<source><![CDATA[Métodos de imagen en el estudio de la mama - Ecografía mamaria]]></source>
<year>2019</year>
<publisher-loc><![CDATA[Bogotá, Colombia ]]></publisher-loc>
<publisher-name><![CDATA[Editorial Medica Panamericana]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B8">
<label>[8]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Freer]]></surname>
<given-names><![CDATA[P. E.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Mammographic breast density: Impact on breast cancer risk and implications for screening]]></article-title>
<source><![CDATA[Radiographics]]></source>
<year>2015</year>
<volume>35</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>302-15</page-range></nlm-citation>
</ref>
<ref id="B9">
<label>[9]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Campáz-Usuga]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Fonnegra]]></surname>
<given-names><![CDATA[R. D.]]></given-names>
</name>
<name>
<surname><![CDATA[Mera]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
</person-group>
<source><![CDATA[Quality Enhancement of Breast DCE-MRI Images Via Convolutional Autoencoders]]></source>
<year></year>
<conf-name><![CDATA[ 2021 IEEE 2nd International Congress of Biomedical Engineering and Bioengineering (CI-IB&amp;BI)]]></conf-name>
<conf-date>2021</conf-date>
<conf-loc>Bogotá D.C., Colombia </conf-loc>
<page-range>1-4</page-range></nlm-citation>
</ref>
<ref id="B10">
<label>[10]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rodríguez Marcano]]></surname>
<given-names><![CDATA[Y. M.]]></given-names>
</name>
<name>
<surname><![CDATA[González]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Palencia]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Sandoval]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[León]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Mamografía espectral con realce de contraste. Nuestra experiencia]]></article-title>
<source><![CDATA[Revista Venezolana de Oncologia]]></source>
<year>2014</year>
<volume>26</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>743-51</page-range></nlm-citation>
</ref>
<ref id="B11">
<label>[11]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pérez-Zúñiga]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Villaseñor-Navarro]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Pérez-Badillo]]></surname>
<given-names><![CDATA[M. P.]]></given-names>
</name>
<name>
<surname><![CDATA[Cruz-Morales]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Pavón-Hernández]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Aguilar-Cortázar]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Resonancia magnética de mama y sus aplicaciones]]></article-title>
<source><![CDATA[Gaceta Mexicana de Oncologia]]></source>
<year>2012</year>
<volume>11</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>268-80</page-range></nlm-citation>
</ref>
<ref id="B12">
<label>[12]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Balleyguier]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[New potential and applications of contrast-enhanced ultrasound of the breast: Own investigations and review of the literature]]></article-title>
<source><![CDATA[Eur. J. Radiol.]]></source>
<year>2009</year>
<volume>69</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>14-23</page-range></nlm-citation>
</ref>
<ref id="B13">
<label>[13]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Valenzuela]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Arevalo]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Tavera]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Riascos]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Bonfante]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Patel]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Imágenes del depósito de gadolinio en el sistema nervioso central]]></article-title>
<source><![CDATA[Revista Chilena de Radiologia]]></source>
<year>Jul.</year>
<month> 2</month>
<day>01</day>
<volume>23</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>59-65</page-range></nlm-citation>
</ref>
<ref id="B14">
<label>[14]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gao]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Chu]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Yoon]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Patel]]></surname>
<given-names><![CDATA[.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis]]></article-title>
<source><![CDATA[IEEE J. Biomed. Health Inform.]]></source>
<year>2020</year>
<volume>24</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>39-49</page-range></nlm-citation>
</ref>
<ref id="B15">
<label>[15]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gao]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis]]></article-title>
<source><![CDATA[Computerized Medical Imaging and Graphics]]></source>
<year>2018</year>
<volume>70</volume>
<page-range>53-62</page-range></nlm-citation>
</ref>
<ref id="B16">
<label>[16]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Image synthesis in contrast MRI based on super resolution reconstruction with multi-refinement cycle-consistent generative adversarial networks]]></article-title>
<source><![CDATA[J. Intell. Manuf.]]></source>
<year>2020</year>
<volume>31</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>1215-28</page-range></nlm-citation>
</ref>
<ref id="B17">
<label>[17]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kim]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Hwan-Ho]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Kwon]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Young-Tack]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Ko]]></surname>
<given-names><![CDATA[E. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Park]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Tumor-Attentive Segmentation-Guided GAN for Synthesizing Breast Contrast-Enhanced MRI Without Contrast Agents]]></article-title>
<source><![CDATA[IEEE Journal of Translational Engineering in Health and Medicine]]></source>
<year>2023</year>
<volume>11</volume>
<page-range>32-43</page-range></nlm-citation>
</ref>
<ref id="B18">
<label>[18]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jiang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Zheng]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Jia]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Song]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Ding]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Synthesis of contrast-enhanced spectral mammograms from low-energy mammograms using cGAN-based synthesis network]]></article-title>
<person-group person-group-type="editor">
<name>
<surname><![CDATA[de Bruijne]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<source><![CDATA[Medical Image Computing and Computer Assisted Intervention - MICCAI 2021]]></source>
<year>2021</year>
<page-range>68-77</page-range><publisher-loc><![CDATA[Cham ]]></publisher-loc>
<publisher-name><![CDATA[Springer International Publishing]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B19">
<label>[19]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Huangz]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Feng]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<source><![CDATA[Understanding Deep Convolutional Networks for Biomedical Imaging: A Practical Tutorial]]></source>
<year></year>
<conf-name><![CDATA[ 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)]]></conf-name>
<conf-date>2019</conf-date>
<conf-loc>Berlin, Germany </conf-loc>
<page-range>857-63</page-range></nlm-citation>
</ref>
<ref id="B20">
<label>[20]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shorten]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Khoshgoftaar]]></surname>
<given-names><![CDATA[T. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A survey on Image Data Augmentation for Deep Learning]]></article-title>
<source><![CDATA[J. Big Data]]></source>
<year>2019</year>
<volume>6</volume>
<numero>1</numero>
<issue>1</issue>
</nlm-citation>
</ref>
<ref id="B21">
<label>[21]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Beers]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[High-resolution medical image synthesis using progressively grown generative adversarial networks]]></source>
<year>2018</year>
<publisher-name><![CDATA[ArXiv: 1805.03144]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B22">
<label>[22]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shen]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Gou]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[F. -Y.]]></given-names>
</name>
</person-group>
<source><![CDATA[Collaborative Adversarial Networks for Joint Synthesis and Segmentation of X-ray Breast Mass Images]]></source>
<year></year>
<conf-name><![CDATA[ 2020 Chinese Automation Congress (CAC)]]></conf-name>
<conf-date>2020</conf-date>
<conf-loc>Shanghai, China </conf-loc>
<page-range>1743-7</page-range></nlm-citation>
</ref>
<ref id="B23">
<label>[23]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Lin]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Qin]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Image-to-Image Translation: Methods and Applications]]></article-title>
<source><![CDATA[IEEE Trans. Multimedia]]></source>
<year>2021</year>
<volume>24</volume>
<page-range>3859-81</page-range></nlm-citation>
</ref>
<ref id="B24">
<label>[24]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Carmen]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Lizandra]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Monserrat]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[José]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Orallo]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Síntesis de Imágenes en Imagen Médica]]></article-title>
<source><![CDATA[Universidad Politécnica de Valencia]]></source>
<year>2003</year>
</nlm-citation>
</ref>
<ref id="B25">
<label>[25]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Anwar]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Difference between AutoEncoder (AE) and Variational AutoEncoder (VAE)]]></source>
<year>2024</year>
<publisher-name><![CDATA[towardsdatascience.com]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B26">
<label>[26]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Weng]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhu]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[INet: Convolutional Networks for Biomedical Image Segmentation]]></article-title>
<source><![CDATA[IEEE Access]]></source>
<year>2021</year>
<volume>9</volume>
<page-range>16591-603</page-range></nlm-citation>
</ref>
<ref id="B27">
<label>[27]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Goodfellow]]></surname>
<given-names><![CDATA[I. J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Generative Adversarial Networks]]></article-title>
<source><![CDATA[Advances in Neural Information Processing Systems]]></source>
<year>2014</year>
<volume>14</volume>
</nlm-citation>
</ref>
<ref id="B28">
<label>[28]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Moreira]]></surname>
<given-names><![CDATA[I. C.]]></given-names>
</name>
<name>
<surname><![CDATA[Amaral]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Domingues]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Cardoso]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Cardoso]]></surname>
<given-names><![CDATA[M. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Cardoso]]></surname>
<given-names><![CDATA[J. S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[INbreast: toward a full-field digital mammographic database]]></article-title>
<source><![CDATA[Acad. Radiol.]]></source>
<year>2012</year>
<volume>19</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>236-48</page-range></nlm-citation>
</ref>
<ref id="B29">
<label>[29]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gao]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Chu]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Yoon]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Patel]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Deep Residual Inception Encoder-Decoder Network for Medical Imaging Synthesis]]></article-title>
<source><![CDATA[IEEE Journal of Biomedical and Health Informatics]]></source>
<year>2020</year>
<volume>24</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>39-49</page-range></nlm-citation>
</ref>
<ref id="B30">
<label>[30]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mori]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Feasibility of new fat suppression for breast MRI using pix2pix]]></article-title>
<source><![CDATA[Jpn. J. Radiol.]]></source>
<year>2020</year>
<volume>38</volume>
<numero>11</numero>
<issue>11</issue>
<page-range>1075-81</page-range></nlm-citation>
</ref>
<ref id="B31">
<label>[31]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Isola]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Jun-Yan]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Efros]]></surname>
<given-names><![CDATA[A. A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Image-to-Image Translation with Conditional Adversarial Networks]]></source>
<year></year>
<conf-name><![CDATA[ 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)]]></conf-name>
<conf-date>2017</conf-date>
<conf-loc>Honolulu, HI, USA </conf-loc>
<page-range>5967-76</page-range></nlm-citation>
</ref>
<ref id="B32">
<label>[32]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Synthesizing the First Phase of Dynamic Sequences of Breast MRI for Enhanced Lesion Identification]]></article-title>
<source><![CDATA[Front. Oncol.]]></source>
<year>2021</year>
<volume>11</volume>
</nlm-citation>
</ref>
<ref id="B33">
<label>[33]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sani]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Prasad]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Hashim]]></surname>
<given-names><![CDATA[E. K. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Breast Cancer Detection in Mammography using Faster Region Convolutional Neural Networks and Group Convolution]]></article-title>
<source><![CDATA[ETE J. Res.]]></source>
<year>2024</year>
<page-range>1-17</page-range></nlm-citation>
</ref>
<ref id="B34">
<label>[34]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fan]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Generative adversarial network-based synthesis of contrast-enhanced MR images from precontrast images for predicting histological characteristics in breast cancer]]></article-title>
<source><![CDATA[Phys. Med. Biol.]]></source>
<year>2024</year>
<volume>69</volume>
<numero>9</numero>
<issue>9</issue>
<page-range>095002</page-range></nlm-citation>
</ref>
<ref id="B35">
<label>[35]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Young-Tack]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
<name>
<surname><![CDATA[Ko]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Park]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<source><![CDATA[TDM-Stargan: Stargan Using Time Difference Map to Generate Dynamic Contrast-Enhanced Mri from Ultrafast Dynamic Contrast-Enhanced Mri]]></source>
<year></year>
<conf-name><![CDATA[ 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)]]></conf-name>
<conf-date>2022</conf-date>
<conf-loc>Kolkata, India </conf-loc>
<page-range>1-5</page-range></nlm-citation>
</ref>
<ref id="B36">
<label>[36]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fujioka]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Proposal to improve the image quality of short-acquisition time-dedicated breast positron emission tomography using the Pix2pix generative adversarial network]]></article-title>
<source><![CDATA[Diagnostics]]></source>
<year>2022</year>
<volume>12</volume>
<numero>12</numero>
<issue>12</issue>
<page-range>3114</page-range></nlm-citation>
</ref>
<ref id="B37">
<label>[37]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jiang]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Lu]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Wei]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Shen]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<source><![CDATA[Synthesize Mammogram from Digital Breast Tomosynthesis with Gradient Guided cGANs]]></source>
<year>2019</year>
<page-range>11769</page-range><publisher-name><![CDATA[Springer International Publishing]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B38">
<label>[38]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Shi]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Fripp]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Bourgeat]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Ea-GANs: Edge-Aware Generative Adversarial Networks for Cross-Modality MR Image Synthesis]]></article-title>
<source><![CDATA[IEEE Transactions on Medical Imaging]]></source>
<year>2019</year>
<volume>38</volume>
<numero>7</numero>
<issue>7</issue>
<page-range>1750-62</page-range></nlm-citation>
</ref>
<ref id="B39">
<label>[39]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Menze]]></surname>
<given-names><![CDATA[B. H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)]]></article-title>
<source><![CDATA[IEEE Transactions on Medical Imaging]]></source>
<year>2015</year>
<volume>34</volume>
<numero>10</numero>
<issue>10</issue>
<page-range>1993-2024</page-range></nlm-citation>
</ref>
<ref id="B40">
<label>[40]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Duque-Arias]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<source><![CDATA[On power jaccard losses for semantic segmentation]]></source>
<year></year>
<conf-name><![CDATA[ Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications]]></conf-name>
<conf-date>2021</conf-date>
<conf-loc>Setúbal, Portugal </conf-loc>
<page-range>561-8</page-range></nlm-citation>
</ref>
<ref id="B41">
<label>[41]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Berman]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Triki]]></surname>
<given-names><![CDATA[A. R.]]></given-names>
</name>
<name>
<surname><![CDATA[Blaschko]]></surname>
<given-names><![CDATA[M. B.]]></given-names>
</name>
</person-group>
<source><![CDATA[The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks]]></source>
<year></year>
<conf-name><![CDATA[ 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition]]></conf-name>
<conf-date>2018</conf-date>
<conf-loc>Salt Lake City, UT, USA </conf-loc>
<page-range>4413-21</page-range></nlm-citation>
</ref>
<ref id="B42">
<label>[42]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<source><![CDATA[Empirical Evaluation of Rectified Activations in Convolutional Network]]></source>
<year>2015</year>
<publisher-name><![CDATA[arXiv:1505.00853]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B43">
<label>[43]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Horé]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Ziou]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<source><![CDATA[Image Quality Metrics: PSNR vs. SSIM]]></source>
<year></year>
<conf-name><![CDATA[ 2010 20th International Conference on Pattern Recognition]]></conf-name>
<conf-date>2010</conf-date>
<conf-loc>Istanbul, Turkey </conf-loc>
<page-range>2366-9</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
